Scaling agentic AI isn’t nearly having the most recent instruments — it requires clear steerage, the precise context, and a tradition that champions experimentation to unlock actual worth. At VentureBeat’s Rework 2025, Anu Bharadwaj, president of Atlassian, shared actionable insights into how the corporate has empowered its workers to construct hundreds of customized brokers that clear up actual, on a regular basis challenges. To construct these brokers, Atlassian has fostered a tradition rooted in curiosity, enthusiasm and steady experimentation.
“You hear quite a bit about AI top-down mandates,” Bharadwaj mentioned. “Prime-down mandates are nice for making an enormous splash, however actually, what occurs subsequent, and to who? Brokers require fixed iteration and adaptation. Prime-down mandates can encourage individuals to begin utilizing it of their every day work, however individuals have to make use of it of their context and iterate over time to appreciate most worth.”
That requires a tradition of experimentation — one the place short- to medium-term setbacks aren’t penalized however embraced as stepping stones to future progress and high-impact use circumstances.
Making a secure surroundings
Atlassian’s agent-building platform, Rovo Studio, serves as a playground surroundings for groups throughout the enterprise to construct brokers.
“As leaders, it’s essential for us to create a psychologically secure surroundings,” Bharadwaj mentioned. “At Atlassian, we’ve at all times been very open. Open firm, no bullshit is one in every of our values. So we give attention to creating that openness, and creating an surroundings the place workers can check out various things, and if it fails, it’s okay. It’s advantageous since you discovered one thing about use AI in your context. It’s useful to be very specific and open about it.”
Past that, it’s a must to create a steadiness between experimentation with guardrails of security and auditability. This contains security measures like ensuring workers are logged in once they’re making an attempt instruments, to creating certain brokers respect permissions, perceive role-based entry, and supply solutions and actions primarily based on what a selected consumer has entry to.
Supporting team-agent collaboration
“After we take into consideration brokers, we take into consideration how people and brokers work collectively,” Bharadwaj mentioned. “What does teamwork appear to be throughout a crew composed of a bunch of individuals and a bunch of brokers — and the way does that evolve over time? What can we do to help that? Consequently, all of our groups use Rovo brokers and construct their very own Rovo brokers. Our concept is that when that sort of teamwork turns into extra commonplace, the whole working system of the corporate modifications.”
The magic actually occurs when a number of individuals work along with a number of brokers, she added. Immediately numerous brokers are single-player, however interplay patterns are evolving. Chat won’t be the default interplay sample, Bharadwaj says. As an alternative, there can be a number of interplay patterns that drive multiplayer collaboration.
“Essentially, what’s teamwork all about?” she posed to the viewers. “It’s multiplayer collaboration — a number of brokers and a number of people working collectively.”
Making agent experimentation accessible
Atlassian’s Rovo Studio makes agent constructing accessible and accessible to individuals of all ability units, together with no-code choices. One development trade buyer constructed a set of brokers to cut back their roadmap creation time by 75%, whereas publishing large HarperCollins constructed brokers that diminished handbook work by 4X throughout their departments.
By combining Rovo Studio with their developer platform, Forge, technical groups acquire highly effective management to deeply customise their AI workflows — defining context, specifying accessible data sources, shaping interplay patterns and extra — and create extremely specialised brokers. On the similar time, non-technical groups additionally have to customise and iterate, in order that they’ve constructed experiences in Rovo Studio to permit customers to leverage pure language to make their customizations.
“That’s going to be the large unlock, as a result of basically, after we speak about agentic transformation, it can’t be restricted to the code gen situations we see as we speak. It has to permeate the whole crew,” Bharadwaj mentioned. “Builders spend 10% of their time coding. The remaining 90% is working with the remainder of the crew, determining buyer points and fixing points in manufacturing. We’re making a platform via which you’ll be able to construct brokers for each single a type of capabilities, so the whole loop will get quicker.”
Making a bridge from right here to the longer term
Not like the earlier shifts to cellular or cloud, the place a set of technological or go-to-market modifications occurred, AI transformation is basically a change in the best way we work. Bharadwaj believes crucial factor to do is to be open and to share how you might be utilizing AI to vary your every day work. “For instance, I share Loom movies of recent instruments that I’ve tried out, issues that I like, issues that I didn’t like, issues the place I assumed, oh, this could possibly be helpful if solely it had the precise context,” she added. “That fixed psychological iteration, for workers to see and check out each single day, is extremely essential as we shift the best way we work.”
